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A Brain Adaptation View of Plasticity: Is Synaptic Plasticity An Overly
Limited Concept?
There is a long tradition, traceable to the early musings
of Ramon y Cajal, of focusing upon the neuron as the only
plastic cell type of any importance within the brain, and
upon the synapse as the only important plastic aspect
regulating the interactions among neurons. While neuronal
plasticity is without question an important aspect of brain
function, it has become increasingly clear that other
cellular elements of brain are plastic and that their
plasticity can contribute to brain function. Moreover, it
is becoming clear in work of others that there are multiple
forms of synaptic plasticity: the synaptic number response
to a complex environment, for example, occurs in animals
genetically rendered incapable of the most common form of
LTP. Our work and that of others indicates that
oligodendrocytes, astrocytes, vasculature, and perhaps
other cellular elements exhibit plasticity quantitatively
equivalent to that of neurons in the developing and mature
brain and that modifications of these cellular elements may
be brought about by experience. It also suggests that
multiple forms of plasticity may occur at the synapse. In
short, while memory researchers largely focus on naturallyand artificially-induced changes in synaptic connectivity,
the brains of real animals (and presumably people) in realworld situations are in a dynamic state in which synaptic
adjustment may in some cases be a relatively small part of
the mix. The intent of this review is to consider the data
that point to this view and consider how we might assess
nonsynaptic effects of learning and/or determine whether
the effects of learning upon measures of brain functional
organization may already be affected by these nonsynaptic
changes.
Since the early speculations of Tanzi and Ramon y
Cajal, the synapse has been the principal proposed site of
plasticity underlying learning and memory in the brain.
Tanzi () initially emphasized the possibility of strength
changes in pre-exiisting connections while Ramon y Cajal ()
stressed the formation and loss of connections. The
ability to examine these possibilities depended on
development of adequate tools, but, by the early 1970s,
electrophysiological and anatomical evidence for the
ability of the nervous system to alter its functional
connectivity in accord with its experience was becoming
reasonably well established. Electrophysiologically, longterm potentiation had been described (Bliss and Lomo,
1973). Anatomically, there was evidence that synapses in
the late developing and adult nervous system could both
form and change in size in response to experience;
innervation of neurons by surviving axons occurred
spontaneously in response to denervation (Sprouting,
Raisman, Lynch references) [when was first comparable
Aplysia report?]; exposure to a complex environment from
weaning through adolescence increased dendritic field
dimensions (Volkmar & Greenough, 1972) and synaptic size
(West & Greenough, 1972) in the rat visual cortex. This
paper summarizes progress in understanding the brain
plasticities thought to be associated with learning and
memory, focusing heavily on our own work, since those
relatively early beginnings.
A relatively early specific demonstration that
synapses formed in response to experience was the report by
Turner and Greenough (1983, 1985) that there were more
synapses per neuron in upper layers of the visual cortex in
rats that had been reared from weaning in a complex
environment. This rearing and adult housing paradigm,
pioneered by Hebb (1949) and his students (e.g. Hymovitch,
19xx) using behavioral measures, and first used as a tool
for exploring brain plasticity by Krech, Rosenzweig and
Bennett (19xx), has been used extensively to examine the
range of plasticity of various elements of brain other than
neurons, but most of these results have failed to become
incorporated into the continuing literature on brain
plasticity. Early in this history, Diamond ** (19xx)
reported that glial cells in visual cortex exhibited
morphological changes in response to experience in paralell
with the changes reported in neuronal dendrites and
synapses. We have subsequently examined plasticity of nonneuronal elements of the cerebral cortex in response to
complex environment exposure in some detail. These results
are addressed at a later point in this chapter.
Following up on these initial demonstrations of
dendritic and synaptic responsiveness to rearing
conditions, neuronal responsiveness to has subsequently
been demonstrated in a wide variety of brain structures:
hippocampus (Mosers et al.), basal ganglia (Comery papers),
cerebellar cortex (Greenough, McDonald, Parnisari, , 19xx)—
someone to fill this in with both our data and others.**
At this point, neuronal plasticity seems to be the rule
rather than the exception in CNS structures, although there
are some striking failures to demonstrate it in some cases
(Kleim LCN; is this the right thing to say? Kleim now has
data showing dentate/interpositus?? plasticity in eyeblink
conditioning.
Similarly, these forms of neuronal plasticity occur
relatively consistently across the age spectrum and do not
appear to be restricted to particular critical or sensitive
periods of development. Effects of adult exposure to a
complex environment have been shown to affect dendritic
field dimensions in the visual cortex of adult, middle-aged
and even elderly rats (e.g., Juraska et al., 1980; Green et
al., 1983; elderly reference). Synapse numbers per neuron
are increased in adults placed in complex environments
(Briones et al., should be written). Likewise, cerebellar
plasticity, while not thoroughly studied, seems to occur
throughout the lifespan, although this may occur in the
context of a decline in elderly animals (e.g., Floeter &
Greenough, 1979; Pysh & Weiss, ? 1979; Greenough Mcd,
parnisari).
It should be briefly noted that there is a reason that the
oft-used term “enriched” is avoided here. These laboratory
environments are simply not enriched relative to the norm
of wild or feral rats. We would argue that no one has
published studies of the brains of rats provided levels of
environmental complexity and challenge beyond the level
provided by the natural environment, and studies of wild
animals have for years confirmed that feral animal brains
are larger than those of domestically reared comparable
animals (old german and other literature, just need one
ref). Nonetheless, studying different degrees of
environmental complexity can provide information about
brain responses that are likely to generalize to higher
levels of stimulation, as suggested also by studies of
human development (e.g., B. Hart and T.R. Risley (1995). Meaningful
Differences in the Everyday Experience of Young American Children. Baltimore:
Brookes Publishing.).
Non-neural forms of brain plasticity
This neuronal/synaptic plasticity is accompanied by
plasticity of non-neural elements such as glia and
vasculature. Subsequent work by Juraska () indicated
enhanced myelination of of splenial axons in EC rats.
Further work has suggested that all type of brain tissue
may exhibit plasticity, at least in regions such as the
cerebral and cerebellar cortices where this has been
examined. Perhaps surprisingly, both in terms of earlier
reports (e.g., Diamond ??64) and in terms of long held
beliefs regarding vascular development (ref, physio book,
from original sirevaag, black paper), the capillary system
exhibits two forms of plasticity: Capillaries are both
larger, on average, and more frequent, it terms of measures
such as the fractional volume of capillaries per neuron, as
shown in Fig. 1 (as we discussed). In fact, on a
percentage change per neuron basis, capillaries exhibit far
more plasticity than synapses, with an increase of over 80%
in volume fraction per neuron in rats placed in complex
environments at weaning (Black, Siervaag & G, 1987;
Sirevaag Black, Shafron & G, 1988; As we noted, check % and
left panel of graph). While the greatest capillary
plasticity is seen in weanlings, this plasticity continues
into adulthood, diminishing with age (Black Polinsky
greenough, 1989).
Similarly, astrocytic plasticity evident in measures
of nuclei (Diamond et al 64?; Sirevaag & Greenough, 1987)
reflects plasticity of astrocytic branching. The extent of
GFAP-immunoreactive astrocytic processes was greater in EC
than in IC rats (Sirevaag & Greenough, 1991). These effects
of complexity that occur in brain areas that exhibit
synaptic plasticity are differentiable from effects of
stress seen, e.g., in the hippocampal formation (Sirevaag
Black Greenough, 1991) and, at least in cerebellar cortex,
parallel plasticity of synapse number (Anderson et al.,
1994; see also Sirevaag & Greenough, 1988 “multivariate).
At a higher level of resolution, astrocytic process
proliferation is more specifically associated with
synapses, which are more completely ensheathed by synapses
in EC rats (Jones & Greenough, 1996), presumably reflecting
the function of optimizing the synaptic microenvironment.
Myelination continues well into human adulthood (ref),
and earlier evidence for experience effects in
oligodendrocyte development came from Szeligo and Leblond
(19xx) and (Kopcik, J. R., P. Seymoure, S. K. Schneider, J. Kim-Hong and J. M.
Juraska Does the distribution of callosal projection neurons reflect the sex difference in
the number of axons in the rat corpus callosum? Brain Research Bulletin, 29: 493-497,
1992.). A recent finding (Briones SFN abstract) indicates that
experience continues to have dramatic effects on the adult
myelination process in the splenial corpus callosum.
Particularly interesting is that these effects of
experience on myelination, as with those for the addition
of synapses (e.g., Camel et al., 19xx; Briones, synapse
adult SFN abst) exhibit a “ratchet” effect: unlike
experience induced changes in glia which appear to fade
rapidly once differential experience is discontinued (e.g.,
Kleim SFN abstract—paper still to be written), changes in
myleination appear to be stable across a subsequent 30 day
period of return to an individual cage housing condition
(Briones SFN abstract; Fig. X). It is interesting to
speculate that added synapses and myelin are stable because
they represent permanent, survival-important additions to
the “wiring diagram” of the brain whereas astrocytic and
vascular (yet to be tested) changes are adjustments to
immediate demands of experience that can be reversed,
saving metabolic investment, in the absence of continued
environmental pressure.
The overriding message, in any case, is that the brain
is the organ of adaptation—-the interface between the
individual and its environment. As such it dynamically
adjusts to the past demands placed upon it by experience,
as if assuming that the experiences of the past must be
good predictors of the future.
What drives the plasticity of brain tissues?
The existence of short-term and long-term processes of
brain cellular adaptation, and the fact that physical
activity and learning are both involved in the behavioral
events that appear to drive these processes, leads to the
next natural question: what causes changes in neurons, glia
and vasculature. At the topmost level (as opposed, say, to
the level of trophic factors or receptor subunits), we can
propose two general causal forces at the behavioral level:
the general categories of activity and learning. With
regard to activity, we have muscle as a model: with
sufficient activity, muscle will hypertrophy, the
particular details varying with the extent and pattern of
activation. One can suppose that activation of brain
tissues in association with peripheral activity, via
intermediary cellular events, might similarly trigger
neuronal, glial or vascular hypertrophy of the sort seen in
rats after complex environment housing. With regard to
learning, it seems plausible that certain changes in
neurons, astrocytes or oligodendrocytes might be learningspecific, although it is harder to imagine that changes in
capillaries would play very specific roles in learning.
At a broader level, it is also possible to imagine
that responses to training such as stress, or related
metabolic consequences of behavioral manipulations could
lead to changes in brain tissue. Certainly stress can have
negative consequences for at least some brain regions
(e.g., Sapolsky), although the adrenal hypertrophycorrelated astroglial changes in the hippocampal formation
appear to be dissociated from the experience-correlated
visual cortex changes in complex environment research
(Sirevaag et al., 1991).
To examine the roles of learning vs. other
consequences of training on neuronal changes, we have
utilized paradigms in which the consequences of learning
would be focused in particular regions in the brain for
which other regions could serve as control or comparison
samples. In one early study, Chang and Greenough (1982)
compared rats trained in a complex series of changing maze
patterns that learned with either the same eye always
occluded or with occlusion of alternate eyes on successive
days. Both groups had been previously subjected to
transection of the corpus callosum, a “split-brain”
procedure that disrupts communication between the two
hemispheres of the brain, such that the unilaterallytrained rats should have most training input restricted to
the hemisphere opposite the open eye, whereas the
bilaterally-alternating training should have allocated the
learning input about equally to both hemispheres. Controls
that were surgically operated and subsequently handled but
not trained were divided into unilaterally and bilaterally
occluded groups. The result indicated increased dendritic
branching, a correlate of increased synapse number, in both
hemispheres of the alternately trained group relative to
the non-trained group and in the non-occluded hemisphere of
the unilaterally trained group, compared to the occluded
hemisphere. This result indicates that either training or
training-related activity drives dendritic plasticity.
A similar study used unilateral vs. bilateral training to
reach with the forelimb into a chamber for food with
similar untrained controls, but without surgical
intervention. The results in this somatosensory-somatomotor
study were similar: for deep pyramidal neurons of the type
that control forelimb activity, dendritic branching was
greater opposite trained forelimbs (Greeno Larson Withers,
85); for more superficial pyramidal neurons, there were
effects of training, but these effects were not restricted
to the “trained” side in unilaterally-trained animals
(Withers et al.,
Withers, G. S., & Greenough, W. T. Reach training
selectively alters dendritic branching in subpopulations of layer II-III pyramids in rat motorsomatosensory forelimb cortex. Neuropsychologia, 27:61-69, 1989.). Taken with the
study above, the results indicate that learning or some
other aspect of training-related activity drives
morphological change in neurons. Both experiments make
clear that a generally-acting hormonal or metabolic effect
would be expected to alter comparable regions of the brain
whether or not they were bein
An obvious issue remaining is that with which we
opened this section: whether activity or learning causes
structural changes in the brain. To address this directly,
Black et al. (1990) created adult female rat groups that
had 1) a substantial amount of learning with relatively
little physical activity (AC below), 2) a substantial
amount of physical activity with relatively little learning
(FX and VX below) or 3) minimal opportunity for physical
activity or learning (IC below). ACrobatic rats (AC)
completed a multi-element elevated obstacle course that
required learning significant motor skill while providing
only limited exercise. Forced eXercise (FX) rats ran on a
treadmill, reaching durations of 60 minutes a day,
exercising but with very little learning. Voluntary
eXercise (VX) rats had access to running wheels attached to
their cages and were the only group to exhibit increased
heart weight, a sign of aerobic exercise. Inactive
Condition (IC) rats were merely removed from their cages
for brief daily experimenter handling, providing neither
activity or learning. Results were clear in initial
studies focusing on cerebellar cortex. As Fig. XA shows,
when blood vessel density was measured, the FX and VX
groups both had more than the AC or IC groups, which did
not differ; this suggests that the formation of new
capillaries was driven by neural activity. By contrast,
when the number of synapses per neuron was measured, shown
in Fig. XB, the learning group, AC, exceeded the other 3
groups, which did not differ, suggesting that when learning
takes place, new synapses are formed.
There is one other interesting thing about these
synapses—many of them involve additional postsynaptic
spines contacting presynaptic varicosities on which one or
more spines already exist (Federmeier et al., submitted). A
similar result has recently been reported by
[Geinisman/Disterhoft] in the ? cerebellar lobule following
associative eyeblink conditioning. In general, according
to [Kristen Harris], spines on a single cerebellar parallel
fiber varicosity tend to arise from the same postsynaptic
cell, such that they are separate parallel lines between a
parallel fiber and a Purkinje (or stellate) neuron. The
implications of this finding for wiring diagram level
models of the learning process remain to be determined. It
should be noted that this phenomenon is not unique to the
cerebellum—we have seen multiple postsynaptic contacts
increased on excitatory morphology presynaptic varicosities
in the visual cortex of rats reared in complex environments
in comparison to caged rats (Jones, T. A., Klintsova, A. Y., Kilman, V. L.,
Sirevaag, A. M. and Greenough, W. T. Induction of multiple synapses by experience in the visual
cortex of adult rats. Neurobiology of Learning and Memory, 68:13-20, 1997.).
Nothing has been added above this line
[TA Jones et al] have also described increases in these
multiple synapses in the intact cortex in the course of
compensatory changes following unilateral cortical lesions,
and a relatively early report indicated increased multiple
postsynaptic innervation of presynaptic terminals
associated with the open eye in monocularly-deprived
[kittens?] (M. Friedlander).
It should be noted that these effects are not limited
to cerebellar cortex. Kleim et al. (papers and absts) have
described synaptogenesis and changes in synapse morphology
in association with the same AC motor learning procedure in
the somatosensory-somatomotor forelimb cortex of rats. The
first morphological change to occur is, on average, an
increase in the size of PSDs, which occurs within one to
two days after training begins. Subsequently, at the next
day examined, day 5, an increase in the number of synapses
per neuron was detected, and the average size of synapses
decreased, possibly because the new synapses were, on
average, smaller than the pre-existing synapse population.
The increase in synapse number was maintained, drifting
slowly, but not statistically, upward across the remainder
of training. As training progressed, the average size of
synapses again increased, possibly suggesting that the new
synapses were growing larger or that the population of
synapses overall was doing so. A schematic interpretation
of these findings appears in Fig. X. There is a long
history of evidence for involvement of synapse size changes
in plasticity that cannot be reviewed here due to space
limitations (is there a Harris or other review to which we
could refer?) (Ask Jeff for input on this paragraph.)
Need a paragraph on perforated synapses. Start with
Greenough west devoogd, Science 1978 EC IC and
developmental age findings. Include Geinisman LTP findings,
other things included in Chapters that Chang & Greenough
did for Cotman and Jones/Peters volumes (Greenough, W. T., & Chang,
F.-L. F. Plasticity of synapse structure and pattern in the cerebral cortex. In E. G. Jones & A.
Peters (Eds.), Cerebral Cortex. Vol. 7. New York: Plenum, 1988, pp. 391-440.) Include Jeff’s
stuff from Motor Cortex, any other recent work that supports this as a possible plasticity
mechanism. We could think about interpretations.
Are there other synapse modifications to be discussed?
Given space constraints I suggest we merely mention vesicle
aggreegate size and some other possible measures and refer to
review articles.
Regulation of Astrocyte Plasticity
Is this redundant? This was mentioned in the section on
non-neuronal plasticity and perhaps should merely be elaborated
more in that section There are two studies that might be
discussed in more detail either in that section or here. One is
Brenda’s 1994? Paper showing the correlation between synapse
number and astrocyte Vv. The other is Jeff’s astrocyte
persistence paper. Tj’s ensheathment paper fits in this
discussion. The point of putting it here is by way of a segue
into a discussion of the tendency to ignore non neuronal (or even
nonsynaptic) changes.
A note on Long-term potentiation
Tell me if this seems out of place. At least 3 studies
dissociate LTP from spatial behavior and morphological change.
The primary point I want to make is the apparent dissociation of
LTP from EC effects on synapses published by J. Tsien in Nature
Neuroscience. This suggests that LTP and synaptogenesis are
independent phenomena. However, Engert and Bonhoefferhave
reported apparent synaptogenesis in vitro in association with LTP
induction. I am not sure what the range of the evidence is or
the weight of it (e.g., other more recent work that bears on this
issue, most of which are likely to have cited both of the above
studies and hence should be locatable via the science citation
index, which I have not used for the last million years), but the
dissociation to me seems most powerful—synapse addition may
mediate LTP, but synapse addition need not involve an LTP-like
process for its induction. If you want to take a crack at this,
feel free. Otherwise I will.
On the Horizon: A Role for Protein Synthesis at the synapse
Since the first report of morphological evidence for
protein synthesis at the synapse (Steward & Levy, 1992) there has
been a growing literature investigating this phenomenon. Synaptic
and dendritic protein synthesis have been shown to be activated
by metabotropic glutamate receptors in some cases (e.g., Weiler &
Greenough, 1993; weiler et al., 1994, 1997; eberwine PNAS—still
in press?) and by NMDA receptors as well (Sheetz et al, 2000).
Proteins synthesized at synapses ibnclude the fragile X protein
FMRP and calcium/calmodulin-dependent protein kinase II (CAMKII).
FMRP has also been shown to be necessary for the mGluR-dependent
synthesis, which is not observed in FMR1 knockout mice (cite
Spangler abstract). Plasticity-inducing forms of electrical
stimulation have been shown to trigger the transcription and
transport of mRNA for the protein ARC to dendritic sites of
stimulation, where it is translated (Steward and Worley
references). mGluR1 activation, ARC synthesis and CAMKII activity
have been proposed to be involved in various forms of plasticity
(Huber/Bear work; Steward; Mary Kennedy), although details of the
specific functions of synaptic or dendritic protein synthesis are
still under investigation. Do you think we need to say anything
more here? The chapter is really not “about” this, and I am not
sure (but open to suggestions) what additional data makes sense
to include.
Summary and Concerns: Synaptic Plasticity and Beyond
The principal thing I want to add at this point is a
summary that comes back to the main point of the chapter--that we
are only looking at a small portion of what the brain does when
it accomplishes plastic change.
CHAPTER WORKING NOTES:
Tissue cultures lacking astrocytes—how good a model?
Lack of astro part of ECM. Lack of basis for TPA, other
actions probably involved in synaptogenesis. MMP3, MMP6,
MMP9 (Metalomatrix proteins), stromolysin, gelatinase.
Roles of Astros, ECM, TPA, etc. in synaptogenesis;
adhesions; rec aggregation
Incorporate Harris, Matus, Segal. Motility and shape
issues. Put together a model, slow accumulation of
synapses via overproduction-selection as a basis for the
stable long-term substrate of memory; plus fast shape
changes, PSD size, perfs, interpret multiple synapses from
local and wiring diagram view.
Also local regulation in dendrites; protein synthesis;
dynamic view; incorporate FMRP in this context
Do also a TINS—go head to head with Menahem